{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get some JSON data from the web - both a point layer and a polygon GeoJson dataset with some population data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas\n",
    "\n",
    "\n",
    "states = geopandas.read_file(\n",
    "    \"https://raw.githubusercontent.com/PublicaMundi/MappingAPI/master/data/geojson/us-states.json\",\n",
    "    driver=\"GeoJSON\",\n",
    ")\n",
    "\n",
    "cities = geopandas.read_file(\n",
    "    \"https://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_50m_populated_places_simple.geojson\",\n",
    "    driver=\"GeoJSON\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And take a look at what our data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>402.504404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1395.100812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.264000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>53.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>234.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10065.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            density\n",
       "count     52.000000\n",
       "mean     402.504404\n",
       "std     1395.100812\n",
       "min        1.264000\n",
       "25%       53.440000\n",
       "50%      100.335000\n",
       "75%      234.050000\n",
       "max    10065.000000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look how far the minimum and maximum values for the density are from the top and bottom quartile breakpoints! We have some outliers in our data that are well outside the meat of most of the distribution. Let's look into this to find the culprits within the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>10065.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>1189.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>1082.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>1006.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>840.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>17.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>9.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Montana</td>\n",
       "      <td>6.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>5.851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>1.264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name    density\n",
       "8   District of Columbia  10065.000\n",
       "30            New Jersey   1189.000\n",
       "51           Puerto Rico   1082.000\n",
       "39          Rhode Island   1006.000\n",
       "21         Massachusetts    840.200\n",
       "31            New Mexico     17.160\n",
       "34          North Dakota      9.916\n",
       "26               Montana      6.858\n",
       "50               Wyoming      5.851\n",
       "1                 Alaska      1.264"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states_sorted = states.sort_values(by=\"density\", ascending=False)\n",
    "\n",
    "states_sorted.head(5).append(states_sorted.tail(5))[[\"name\", \"density\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like Washington D.C. and Alaska were the culprits on each end of the range. Washington was more dense than the next most dense state, New Jersey, than the least dense state, Alaska was from Wyoming, however. Washington D.C. has a has a relatively small land area for the amount of people that live there, so it makes sense that it's pretty dense. And Alaska has a lot of land area, but not much of it is habitable for humans.\n",
    "<br><br>\n",
    "However, we're looking at all of the states in the US to look at things on a more regional level. That high figure at the top of our range for Washington D.C. will really hinder the ability for us to differentiate between the other states, so let's account for that in the min and max values for our color scale, by getting the quantile values close to the end of the range. Anything higher or lower than those values will just fall into the 'highest' and 'lowest' bins for coloring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimum: 8.54\n",
      "\n",
      "maximum: 1040.2\n",
      "\n",
      "Mean: 402.5\n"
     ]
    }
   ],
   "source": [
    "def rd2(x):\n",
    "    return round(x, 2)\n",
    "\n",
    "\n",
    "minimum, maximum = states[\"density\"].quantile([0.05, 0.95]).apply(rd2)\n",
    "\n",
    "mean = round(states[\"density\"].mean(), 2)\n",
    "\n",
    "\n",
    "print(f\"minimum: {minimum}\", f\"maximum: {maximum}\", f\"Mean: {mean}\", sep=\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks better. Our min and max values for the colorscale are much closer to the mean value now. Let's run with these values, and make a colorscale. I'm just going to use a sequential light-to-dark color palette from the [ColorBrewer](http://colorbrewer2.org/#type=sequential&scheme=Purples&n=5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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style=\"stroke:#54278fff;stroke-width:3;\" /><line x1=\"494\" y1=\"0\" x2=\"494\" y2=\"20\" style=\"stroke:#54278fff;stroke-width:3;\" /><line x1=\"495\" y1=\"0\" x2=\"495\" y2=\"20\" style=\"stroke:#54278fff;stroke-width:3;\" /><line x1=\"496\" y1=\"0\" x2=\"496\" y2=\"20\" style=\"stroke:#54278fff;stroke-width:3;\" /><line x1=\"497\" y1=\"0\" x2=\"497\" y2=\"20\" style=\"stroke:#54278fff;stroke-width:3;\" /><line x1=\"498\" y1=\"0\" x2=\"498\" y2=\"20\" style=\"stroke:#54278fff;stroke-width:3;\" /><line x1=\"499\" y1=\"0\" x2=\"499\" y2=\"20\" style=\"stroke:#54278fff;stroke-width:3;\" /><text x=\"0\" y=\"35\">8.54</text><text x=\"500\" y=\"35\" style=\"text-anchor:end;\">1040.2</text></svg>"
      ],
      "text/plain": [
       "<branca.colormap.LinearColormap at 0x7f2e86fcb250>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import branca\n",
    "\n",
    "\n",
    "colormap = branca.colormap.LinearColormap(\n",
    "    colors=[\"#f2f0f7\", \"#cbc9e2\", \"#9e9ac8\", \"#756bb1\", \"#54278f\"],\n",
    "    index=states[\"density\"].quantile([0.2, 0.4, 0.6, 0.8]),\n",
    "    vmin=minimum,\n",
    "    vmax=maximum,\n",
    ")\n",
    "\n",
    "colormap.caption = \"Population Density in the United States\"\n",
    "\n",
    "colormap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's narrow down these cities to United states cities, by using GeoPandas' spatial join functionality between two GeoDataFrame objects, using the Point 'within' Polygon functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "us_cities = geopandas.sjoin(cities, states, how=\"inner\", predicate=\"within\")\n",
    "\n",
    "pop_ranked_cities = us_cities.sort_values(by=\"pop_max\", ascending=False)[\n",
    "    [\"nameascii\", \"pop_max\", \"geometry\"]\n",
    "].iloc[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now we have a new GeoDataFrame with our top 20 populated cities. Let's see the top 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nameascii</th>\n",
       "      <th>pop_max</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>New York</td>\n",
       "      <td>19040000</td>\n",
       "      <td>POINT (-73.98196 40.75192)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>12500000</td>\n",
       "      <td>POINT (-118.18193 33.99192)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>8990000</td>\n",
       "      <td>POINT (-87.75200 41.83194)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>Miami</td>\n",
       "      <td>5585000</td>\n",
       "      <td>POINT (-80.22605 25.78956)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>5492000</td>\n",
       "      <td>POINT (-75.17194 40.00192)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         nameascii   pop_max                     geometry\n",
       "1224      New York  19040000   POINT (-73.98196 40.75192)\n",
       "1222   Los Angeles  12500000  POINT (-118.18193 33.99192)\n",
       "1186       Chicago   8990000   POINT (-87.75200 41.83194)\n",
       "1184         Miami   5585000   POINT (-80.22605 25.78956)\n",
       "1076  Philadelphia   5492000   POINT (-75.17194 40.00192)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_ranked_cities.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, let's build a map!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f2e86d9b2b0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "from folium.plugins import Search\n",
    "\n",
    "\n",
    "m = folium.Map(location=[38, -97], zoom_start=4)\n",
    "\n",
    "\n",
    "def style_function(x):\n",
    "    return {\n",
    "        \"fillColor\": colormap(x[\"properties\"][\"density\"]),\n",
    "        \"color\": \"black\",\n",
    "        \"weight\": 2,\n",
    "        \"fillOpacity\": 0.5,\n",
    "    }\n",
    "\n",
    "\n",
    "stategeo = folium.GeoJson(\n",
    "    states,\n",
    "    name=\"US States\",\n",
    "    style_function=style_function,\n",
    "    tooltip=folium.GeoJsonTooltip(\n",
    "        fields=[\"name\", \"density\"], aliases=[\"State\", \"Density\"], localize=True\n",
    "    ),\n",
    ").add_to(m)\n",
    "\n",
    "citygeo = folium.GeoJson(\n",
    "    pop_ranked_cities,\n",
    "    name=\"US Cities\",\n",
    "    tooltip=folium.GeoJsonTooltip(\n",
    "        fields=[\"nameascii\", \"pop_max\"], aliases=[\"\", \"Population Max\"], localize=True\n",
    "    ),\n",
    ").add_to(m)\n",
    "\n",
    "statesearch = Search(\n",
    "    layer=stategeo,\n",
    "    geom_type=\"Polygon\",\n",
    "    placeholder=\"Search for a US State\",\n",
    "    collapsed=False,\n",
    "    search_label=\"name\",\n",
    "    weight=3,\n",
    ").add_to(m)\n",
    "\n",
    "citysearch = Search(\n",
    "    layer=citygeo,\n",
    "    geom_type=\"Point\",\n",
    "    placeholder=\"Search for a US City\",\n",
    "    collapsed=True,\n",
    "    search_label=\"nameascii\",\n",
    ").add_to(m)\n",
    "\n",
    "folium.LayerControl().add_to(m)\n",
    "colormap.add_to(m)\n",
    "\n",
    "m"
   ]
  }
 ],
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